Independent component analysis (ICA) is a widely used method in various applications of signal processing and feature extraction. It extends principal component analysis (PCA) and can extract important and complicated components with small variances. One of the major problems of ICA is that the uniqueness of the solution is not guaranteed, unlike PCA. That is because there are many local optima in optimizing the objective function of ICA. It has been shown previously that the unique global optimum of ICA can be estimated from many random initializations by handcrafted thread computation. In this paper, the unique estimation of ICA is highly accelerated by reformulating the algorithm in matrix representation and reducing redundant calculations. Experimental results on artificial datasets and EEG data verified the efficiency of the proposed method.

翻译：独立成分分析（ICA）是信号处理和特征提取领域中广泛应用的一种方法。它扩展了主成分分析（PCA），能够提取方差较小的重要且复杂的成分。与PCA不同，ICA的一个主要问题是其解的唯一性无法得到保证。这是因为在优化ICA的目标函数时存在许多局部最优解。先前的研究表明，可以通过手工设计的线程计算从多次随机初始化中估计出ICA的唯一全局最优解。本文通过将算法重新表述为矩阵表示并减少冗余计算，极大地加速了ICA的唯一性估计。在人工数据集和脑电图数据上的实验结果验证了所提方法的效率。